Over the last fifty years, How to Lie with Statistics has sold more copies than any other statistical text. This note explores the factors that contributed to its success and provides biographical sketches of its creators: author Darrell Huff and illustrator Irving Geis.

Darrell Huff’s How to Lie with Statistics remains the best-known, nontechnical call for critical thinking about statistics. However, drawing a distinction between statistics and lying ignores the process by which statistics are socially constructed. For instance, bad statistics often are disseminated by sincere, albeit innumerate advocates (e.g., inflated estimates for the number of anorexia deaths) or through research findings selectively highlighted to attract media coverage (e.g., a recent study on the extent of bullying). Further, the spread of computers has made the production and dissemination of dubious statistics easier. While critics may agree on the desirability of increasing statistical literacy, it is unclear who might accept this responsibility.

Darrell Huff’s How to Lie with Statistics was the inspiration for How to Lie with Maps, in which the author showed that geometric distortion and graphic generalization of data are unavoidable elements of cartographic representation. New examples of how ill-conceived or deliberately contrived statistical maps can greatly distort geographic reality demonstrate that lying with maps is a special case of lying with statistics. Issues addressed include the effects of map scale on geometry and feature selection, the importance of using a symbolization metaphor appropriate to the data and the power of data classification to either reveal meaningful spatial trends or promote misleading interpretations.

This article shows by various examples how consumers of statistical information may be confused when this information is presented in terms of conditional probabilities. It also shows how this confusion helps others to lie with statistics, and it suggests both confusion and lies can be exposed by using alternative modes of conveying statistical information.

As Huff’s landmark book made clear, lying with statistics can be accomplished in many ways. Distorting graphics, manipulating data or using biased samples are just a few of the tried and true methods. Failing to use the correct statistical procedure or failing to check the conditions for when the selected method is appropriate can distort results as well, whether the motives of the analyst are honorable or not. Even when the statistical procedure and motives are correct, bad data can produce results that have no validity at all. This article provides some examples of how bad data can arise, what kinds of bad data exist, how to detect and measure bad data, and how to improve the quality of data that have already been collected.

We’ve known how to lie with statistics for 50 years now. What we really need are theory and praxis for accusing someone else of lying with statistics. The author’s experience with the response to The Bell Curve has led him to suspect that such a formulation already exists, probably imparted during a secret initiation for professors in the social sciences. This article represents his best attempt to reconstruct what must be in it.

In February 2004, the U.S. Food and Drug Administration (FDA) prohibited the sale of dietary supplements containing ephedrine alkaloids (ephedra), stating that such supplements present an unreasonable risk of illness or injury. The Dietary Supplement Health and Education Act (DSHEA) of 1994 (21 USC §301, 1994) governs dietary supplement regulation in the U.S. DSHEA places the burden of proof for safety on the government rather than on the manufacturer and thus differs significantly from regulations that govern the marketing of drugs. Part of the evidence the FDA used in reaching its decision was a systematic review of the efficacy and safety of ephedra conducted by the Southern California Evidence-Based Practice Center. In addition to a meta-analysis of controlled trial data, the review contained an evaluation of observational case report data, a study design that has limited inferential abilities regarding cause and effect.

How did the FDA decide what data were relevant to its decision? How did the FDA argument for the ban differ from a decision based solely on statistical hypothesis testing? This paper will address these questions by describing the systematic review approach, the evidence presented, the interpretation of that evidence by those on both sides of the argument and the process by which the decision was made.

In the wake of controversy over allegations of espionage by Wen Ho Lee, a nuclear scientist at the Department of Energy’s Los Alamos National Laboratory, the department ordered that polygraph tests be given to scientists working in similar positions. Soon thereafter, at the request of Congress, the department asked the National Research Council (NRC) to conduct a thorough study of polygraph testing’s ability to distinguish accurately between lying and truth-telling across a variety of settings and examinees, even in the face of countermeasures that may be employed to defeat the test. This paper tells some of the story of the work of the Committee to Review the Scientific Evidence on the Polygraph, its report and the reception of that report by the U.S. government and Congress.

The pretest–posttest study is commonplace in numerous applications. Typically, subjects are randomized to two treatments, and response is measured at baseline, prior to intervention with the randomized treatment (pretest), and at prespecified follow-up time (posttest). Interest focuses on the effect of treatments on the change between mean baseline and follow-up response. Missing posttest response for some subjects is routine, and disregarding missing cases can lead to invalid inference. Despite the popularity of this design, a consensus on an appropriate analysis when no data are missing, let alone for taking into account missing follow-up, does not exist. Under a semiparametric perspective on the pretest–posttest model, in which limited distributional assumptions on pretest or posttest response are made, we show how the theory of Robins, Rotnitzky and Zhao may be used to characterize a class of consistent treatment effect estimators and to identify the efficient estimator in the class. We then describe how the theoretical results translate into practice. The development not only shows how a unified framework for inference in this setting emerges from the Robins, Rotnitzky and Zhao theory, but also provides a review and demonstration of the key aspects of this theory in a familiar context. The results are also relevant to the problem of comparing two treatment means with adjustment for baseline covariates.

Walter Theodore Federer was born on August 23, 1915 in Cheyenne, Wyoming. He received a B.S. in agronomy from Colorado State University in 1939. In 1941 he received the M.S. degree in plant breeding from Kansas State University. His Ph.D. in mathematical statistics from Iowa State University was awarded in 1948. He held the position of Associate Geneticist while working on the Guayule Research Project, USDA, in Salinas, California. While completing his Ph.D. at Iowa State University, he was Associate Statistician in the Agricultural Marketing Service in Ames, Iowa. His next position was as Professor of Biological Statistics in the College of Agricultural and Life Sciences at Cornell University. He was also the Administrator of the Biometrics Unit in the Department of Plant Breeding. He was awarded the Liberty Hyde Bailey Professor of Statistics Chair in 1978, which he held until retirement in 1986, since which he has had emeritus status.

Dr. Federer was Secretary and Program Coordinator for the Eastern North American Region (ENAR) of the International Biometric Society from 1950 to 1953, President-Elect of ENAR in 1959 and President in 1960. He was Chairman and Executive Secretary of the Committee of Presidents of Statistical Societies (1965–1972), Book Reviews Editor (1964–1972) and Associate Editor for Biometrics (1972–1976), Associate Editor for Communications in Statistics (1972–1994) and Associate Editor for the Journal of Statistical Planning and Inference (1976–1990). He was a member of national, international, university and government panels and boards, and was a consultant for several international research stations.

Dr. Federer is a Fellow of the American Statistical Association (1958), American Association for the Advancement of Science (1962), Royal Statistical Society (1964) and the Institute of Mathematical Statistics (1967), and was elected a Member of the International Statistical Institute (1974). He was awarded the Honor Alumnus Achievement Award (1972) and Honored Alumnus Award (2001) by Colorado State University, and the Distinguished Service in Agriculture Award (1988) by Kansas State University.